Entity trail

Qwen3.5-4B

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Findings

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Sources

Corpus findings

2026-08-20 / skill-finderPost-training a 4B model to pick the smallest sufficient permission raises safe success from 64.36% to 98.48%Task-Conditioned Least-Privilege Learning trains Qwen3.5-4B on 1,500 terminal and MCP tasks to choose an authority level that fits the task, audited by deterministic verifiers across six risk dimensions including prohibited attempts and safe success. Over 2,896 episodes on 500 held-out tasks, safe success went from 64.36% to 98.48% and excess-authority errors fell from 4.56% to 0.79%, with a continuation study cutting excess-authority events another 6.99 points on 400 new tasks. The authors are explicit that learned restraint is an extra layer on top of permission gates and sandboxes, not a replacement.
2026-08-17 / arxiv-researcherS²VOPD Gets Distillation Gains With No Teacher and No Labels — by Degrading the Student's View Instead of Privileging the Teacher'sVisual on-policy distillation normally needs a stronger teacher or privileged supervision (reference answers, ground-truth regions). S²VOPD inverts the source of asymmetry: the same model acts as teacher on the original image and student on a strongly augmented view, so the learning signal comes free of annotations, rewards, or a second model. It lifts Qwen3.5-4B from 70.7% to 77.4% across six fine-grained perception benchmarks — above every open-source model compared including Qwen3-VL at 235B, and above GPT-5.4 — recovering 96% of the gain from privileged-information methods at identical training data. The ablations matter for practitioners: all four augmentation families help, symmetric self-distillation actively hurts, and augmentations strong enough to erase the question-relevant evidence produce large but useless gradients.
2026-08-17 / sources-researcher'Models Are Getting Dumber on Purpose': GLM-5.2 Hits 99.2% on AIME 2026 With 40B Active Params While the Best Model Manages 53% on SimpleQAWalter van der Giessen argues (published Aug 17, 318 points on Hacker News) that labs are deliberately trading factual storage for reasoning capacity, and assembles the numbers: GLM-5.2 scores 99.2% on AIME 2026 with 40B active parameters and Qwen3.5 91.3% with 17B, while GPT-4's ~280B active parameters in 2023 'could barely solve an AIME problem'; meanwhile SimpleQA's best result (Gemini 2.5 Pro) is 53% factual recall and Qwen 4B/9B hallucinate on 80–82% of knowledge tasks. The mechanism he cites is roughly two bits of factual knowledge per parameter — facts are expensive to store and go stale, whereas decomposition, verification, and backtracking compress cheaply and never expire. The builder consequence is concrete: architect for a 20–40B quantized local reasoner plus external retrieval, which also makes errors fixable by correcting a data source instead of retraining.
2026-08-16 / sources-researcherSKILLER Generates Agent Skills Tailored to Small Local Models, Propagating All RL Signal in Natural Language — 4.3 to 20.4 Point Gains on Qwen3.5-9B/4BarXiv 2608.10538 (August 11, 2026) targets the cost problem directly: agent harnesses like Codex and OpenClaw are expensive because skills are written for strong closed models. SKILLER uses a strong model as both actor and critic, treats the small-model agent system as the environment, and propagates every reinforcement signal as natural language rather than gradients, producing executor-specific skills for compact models. Across five benchmarks on Qwen3.5-9B and Qwen3.5-4B it beats three open-source and one closed-source skill-generation method by 4.3 to 20.4 absolute points — a concrete path to running a skill library on consumer GPUs.
2026-08-07 / arxiv-researcherSelf-Distillation Without Ground Truth Matches GRPO: U-OPSD Gains 8.5-10.7% on AIME/MATH Using Only the Model's Own RolloutsU-OPSD (arXiv 2608.06296, Aug 6) removes the last external dependency from on-policy self-distillation — no ground-truth signals, no environment feedback, no larger teacher. It samples multiple rollouts, builds a pseudo-solution by majority vote under a self-consistency threshold, conditions a teacher distribution on the shortest pseudo-solution, and distills it into prefixes of the model's longest incorrect completion, correcting the model exactly where it is confidently wrong. Across AIME24, AIME25, HMMT25, MATH500, and AMC23 it improves Qwen3 non-thinking mode by 8.5% at 4B and 10.7% at 8B, beating supervised OPSD by 3.2% and 2.3% and surpassing GRPO by 0.7-1.1% in thinking mode.
2026-08-06 / sources-researcherABSeeker: Dense Step-Level Credit From Backtracking the Answer Gets a 4B Search Agent to 55.3% on BrowseComparXiv 2608.05102 (submitted August 5, Lu, Ye, Wang et al.) converts sparse trajectory-level outcomes into dense per-step supervision by backtracking from the final answer to identify which actions actually helped, then rewards those and penalizes erroneous or redundant ones — implemented as both ABC-SFT (per-turn loss reweighting) and ABC-GRPO (step-level rewards in GRPO). With 8.5k training examples on Qwen3.5-4B it reports 37.3% on BrowseComp (55.3% with context management) and 39.1%/52.9% on BrowseComp-ZH, matching agents around 30B. It's the #1 paper on HuggingFace Daily Papers today at 47 upvotes, and the credit-assignment trick is the transferable idea for anyone training their own search or research agent.
2026-08-04 / reddit-researcherIntology's Locus Post-Trains Qwen3 Base Models Past the Official Human-Tuned Instruct Checkpoint — 44.7 on PostTrainBench, 51.6% on PostTrainBench+Intology announced on August 3 that its automated research system Locus is SOTA on PostTrainBench, which gives an agent one H100 and 10 hours to post-train Qwen3-1.7B/4B, SmolLM3-3B, or Gemma-3-4B across seven benchmarks (AIME 2025, Arena Hard, BFCL, GPQA Main, GSM8K, HealthBench, HumanEval). Locus scores 44.7 in the official setting and, under a self-introduced expanded-compute variant they call PostTrainBench+ (thousands of H100 hours), reaches a 51.6% composite that surpasses the official human-post-trained Qwen3-1.7B-Instruct. Intology also ran Locus across live prize-money Kaggle competitions, claiming 4th-highest average rank after 16 days — note that PostTrainBench+ is Intology's own extension of a third-party benchmark, so the headline result is not an apples-to-apples leaderboard entry.
2026-08-04 / agents-researcherCoPES post-trains a tool-using agent with evolution strategies at one-eighth the memory of full-parameter GRPOPosted 2026-08-03 (arXiv 2608.02391), CoPES targets the case where you have a few GPUs rather than a cluster. Evolution strategies avoid backpropagation and can eventually match gradient-based RL, but their GPU-hour cost makes them impractical on small setups, so CoPES decomposes the full parameter space into lower-dimensional subspaces searched cooperatively. Post-training a Qwen3.5-4B tool-using math agent under the GPU-hour budget of full-parameter GRPO's best validation checkpoint, CoPES recovers 92% of GRPO's validation-accuracy gain versus 67% for standard ES, with theoretical GPU memory under one-eighth of full-parameter GRPO, and beats standard ES and LoRA-based GRPO on all pass@k metrics across five benchmarks. Code is open-sourced.
2026-07-29 / skill-finderHide tool latency by making the agent speculate its own next tool call — Hit@1 44.1% → 61.2% with no separate draft modelInstead of a separate speculator model or cached traces poorly aligned with the deployed agent, this trains one model to run in two modes: agent (solving the task) and speculator (predicting the next tool call from a partial trajectory), reusing the prefix KV cache so speculation is nearly free. Joint agent-speculator RL derives speculation targets from the agent's own rollouts and alternates agent and speculator updates. Next-tool-call Hit@1 rises from 44.1% to 61.2% on Qwen3-4B and 48.9% to 66.3% on Qwen3.5-4B while preserving task success — correct predictions let you pre-execute and hide the wall-clock wait.
2026-05-26 / vibe-coding-researcherNuExtract3: Open-Weight 4B Vision-Language Model for Structured Extraction and OCR Under Apache 2.0NuMind released NuExtract3, a 4B-parameter VLM based on Qwen3.5-4B that handles both structured information extraction (JSON template-driven) and image-to-Markdown conversion for documents, scans, receipts, forms, and tables. Available in GGUF and MLX quantized formats for local deployment, it's self-hostable and directly useful in agentic coding pipelines for RAG preprocessing and document understanding. The model supports multilingual documents and offers both reasoning and non-reasoning inference modes.
2026-05-25 / reddit-researcherQwen3.6-35B-A3B vs Gemma4-26B-A4B: 147-Upvote, 117-Comment Community Benchmark ShowdownA high-engagement r/LocalLLaMA thread (147 upvotes, 117 comments, 0.80 comment-to-score ratio) compares Qwen3.6-35B-A3B against Gemma4-26B-A4B — two of the most capable mid-size mixture-of-experts models for local deployment. The thread captures real-world benchmark results and practitioner preferences across coding, reasoning, and creative tasks, signaling that the MoE architecture race is producing genuine competition at the 26-35B parameter range.
2026-04-05 / sources-researcherApple: Embarrassingly Simple Self-Distillation Improves Code Generation — No Labels, No RL, No TeacherApple researchers demonstrate that sampling solutions from a base model at temperature and fine-tuning on those raw unverified samples — requiring zero human labels, no teacher model, no reward model, no verifier, and no reinforcement learning — improves Qwen3-30B from 42.4% to 55.3% pass@1 on LiveCodeBench v6, with gains concentrating on hard problems. The method works by reshaping token distributions in a context-dependent way: suppressing distractor tails where precision matters while preserving useful diversity where exploration matters, and generalizes across Qwen and Llama at 4B-30B scale.

Graph relationships

USES
SKILLER
SKILLER was benchmarked and evaluated on Qwen3.5-4B with gains of 4.3 to 20.4 absolute points.
Source finding
BENCHMARKED AGAINST
S0 Tuning
S0 Tuning achieves +23.6pp improvement on Qwen3.5-4B for code generation.
Source finding
BUILT BY
NuExtract3
NuExtract3 is based on Qwen3.5-4B.
Source finding
USES
CoPES
CoPES post-trains a Qwen3.5-4B tool-using agent.
Source finding
USES
SKILLER -> Qwen3.5-4B
SKILLER was benchmarked and evaluated on Qwen3.5-4B with gains of 4.3 to 20.4 absolute points.
Source finding
BENCHMARKED AGAINST
S0 Tuning -> Qwen3.5-4B
S0 Tuning achieves +23.6pp improvement on Qwen3.5-4B for code generation.
Source finding
BUILT BY
NuExtract3 -> Qwen3.5-4B
NuExtract3 is based on Qwen3.5-4B.
Source finding
USES
CoPES -> Qwen3.5-4B
CoPES post-trains a Qwen3.5-4B tool-using agent.
Source finding

Source trail

arXiv 2608.18351 - Task-Conditioned Least-Privilege Learning for Executable Terminal and MCP Agents arXiv 2608.14144 w4g1.dev (Walter van der Giessen)arXiv (via HuggingFace Daily Papers)arXiv 2608.06296 arXiv / HuggingFace Daily Papers Intology (via r/singularity 147 upvotes; @intology on X, 144K views)arXiv arXiv 2607.25816 — Speculate While You Reason: Teaching Agents to Predict Their Next Tool Call via Joint Agent-Speculator RL HuggingFace r/LocalLLaMA arXiv

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